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Siddarth Singaravel

Bio: Siddarth Singaravel is an academic researcher from VIT University. The author has contributed to research in topics: Compiler & Optimizing compiler. The author has co-authored 1 publications.

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Journal ArticleDOI
01 Jan 2021
TL;DR: This article aims to provide an overall survey of the cache optimization methods, multi memory allocation features and explore the scope of machine learning in compiler optimization to attain a sustainable computing experience for the developer and user.
Abstract: Compiler optimization techniques allow developers to achieve peak performance with low-cost hardware and are of prime importance in the field of efficient computing strategies. The realm of compiler suites that possess and apply efficient optimization methods provide a wide array of beneficial attributes that help programs execute efficiently with low execution time and minimal memory utilization. Different compilers provide a certain degree of optimization possibilities and applying the appropriate optimization strategies to complex programs can have a significant impact on the overall performance of the system. This paper discusses methods of compiler optimization and covers significant advances in compiler optimization techniques that have been established over the years. This article aims to provide an overall survey of the cache optimization methods, multi memory allocation features and explore the scope of machine learning in compiler optimization to attain a sustainable computing experience for the developer and user.
Journal Article
TL;DR: In this paper , supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset and the results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient.
Abstract: Water is known as a "universal solvent" as it is extraordinarily frail against contamination. Water quality standards are developed based on logical evidence on the effects of hazardous compounds on a certain quantity of water used. Classification technique of machine learning can be employed to under-stand the water quality status. In this work, supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset. Artificial neural net-work model is built using the features such as Oxygen, pH, temperature, total suspended sediment, turbidity, nitrogen, and phosphorus as inputs and water quality check as target variable. This target variable is created using Canadian Council of Ministers of the Environment Water Quality Index, and the model works with an accuracy of 87%. The classification is done on XGBoost model as well and it performs with an accuracy of 90%. The explanations for predictions of these models for a data instance were performed using explainable artificial intelligence tools such as LIME and SHAP. The results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient. Through our re-search we can benefit our readers by providing them clarity about exactly what features are having more influence on water quality than others from different machine learning algorithms. This will help the developers to gain insights about the significant factors of poor water quality and how to overcome that.